Few-shot graph and smiles learning for molecular property prediction

Dan Sun

doi:10.1117/12.3005809

10 October 2023 Few-shot graph and smiles learning for molecular property prediction

Dan Sun

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127991J (2023) https://doi.org/10.1117/12.3005809
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Molecular property prediction can be applied to discover new drugs, which has attracted significant attention from both chemists and machine learning researchers. However, the existing methods for predicting molecular properties have the following limitations. The molecular representions learned from 2D structures will lose some important information contained in the molecular representions learned from 1D SMILES strings and vice versa; Second, the few-shot learning for molecular property prediction ignores the great differences between properties of different moleculars. To address the above problems, we propose a molecular property prediction model based on few-shot learning, called FewGS in this paper. We present a molecular graph-string combination method to fuse SMILES and graph represention, which can exploit a small amount of available molecular information to capture the hidden feature of moleculars. To promote the few-shot learning for molecular property prediction, we also propose a sample space transformation strategy to effectively eliminate bias between meta-train data and meta-test data. In addition, we construct a loss function based on graph-string combination, and adjust the weights through a self-attentive mechanism to achieve accurate prediction. We evaluate FewGS on molecular datasets and experimental results show that FewGS achieves state-of-the-art performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dan Sun "Few-shot graph and smiles learning for molecular property prediction", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127991J (10 October 2023); https://doi.org/10.1117/12.3005809

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KEYWORDS

Machine learning

Molecules

Education and training

Neural networks

Feature extraction

Chemical species

Data modeling

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